



import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXModel,
		)





class 			__magic_name__ :






		def __init__(    self   :  Optional[Any]					,		UpperCamelCase__   :  Dict					,		UpperCamelCase__   :  Any=13					,		UpperCamelCase__   :  List[str]=7					,		UpperCamelCase__   :  str=True					,		UpperCamelCase__   :  Union[str, Any]=True					,		UpperCamelCase__   :  str=True					,		UpperCamelCase__   :  Optional[int]=True					,		UpperCamelCase__   :  Tuple=99					,		UpperCamelCase__   :  Optional[int]=64					,		UpperCamelCase__   :  int=5					,		UpperCamelCase__   :  Tuple=4					,		UpperCamelCase__   :  Dict=37					,		UpperCamelCase__   :  int="gelu"					,		UpperCamelCase__   :  Union[str, Any]=0.1					,		UpperCamelCase__   :  List[Any]=0.1					,		UpperCamelCase__   :  List[str]=5_12					,		UpperCamelCase__   :  Union[str, Any]=16					,		UpperCamelCase__   :  List[Any]=2					,		UpperCamelCase__   :  int=0.02					,		UpperCamelCase__   :  Union[str, Any]=3					,		UpperCamelCase__   :  int=4					,		UpperCamelCase__   :  int=None					,		)					->       Tuple:
								'''simple docstring'''







								UpperCAmelCase								=  parent
								UpperCAmelCase								=  batch_size
								UpperCAmelCase								=  seq_length
								UpperCAmelCase								=  is_training
								UpperCAmelCase								=  use_input_mask
								UpperCAmelCase								=  use_token_type_ids
								UpperCAmelCase								=  use_labels
								UpperCAmelCase								=  vocab_size
								UpperCAmelCase								=  hidden_size
								UpperCAmelCase								=  num_hidden_layers
								UpperCAmelCase								=  num_attention_heads
								UpperCAmelCase								=  intermediate_size
								UpperCAmelCase								=  hidden_act
								UpperCAmelCase								=  hidden_dropout_prob
								UpperCAmelCase								=  attention_probs_dropout_prob
								UpperCAmelCase								=  max_position_embeddings
								UpperCAmelCase								=  type_vocab_size
								UpperCAmelCase								=  type_sequence_label_size
								UpperCAmelCase								=  initializer_range
								UpperCAmelCase								=  num_labels
								UpperCAmelCase								=  num_choices
								UpperCAmelCase								=  scope
								UpperCAmelCase								=  vocab_size - 1






		def SCREAMING_SNAKE_CASE_      (    self   :  int   )					->       Optional[int]:
								'''simple docstring'''







								UpperCAmelCase								=  ids_tensor([self.batch_size, self.seq_length]					,		self.vocab_size   )

								UpperCAmelCase								=  None
								if self.use_input_mask:
														UpperCAmelCase								=  random_attention_mask([self.batch_size, self.seq_length]   )

								UpperCAmelCase								=  None
								if self.use_labels:
														UpperCAmelCase								=  ids_tensor([self.batch_size, self.seq_length]					,		self.num_labels   )

								UpperCAmelCase								=  self.get_config()

								return config, input_ids, input_mask, token_labels






		def SCREAMING_SNAKE_CASE_      (    self   :  int   )					->       str:
								'''simple docstring'''







								return GPTNeoXConfig(
								    vocab_size=self.vocab_size					,		hidden_size=self.hidden_size					,		num_hidden_layers=self.num_hidden_layers					,		num_attention_heads=self.num_attention_heads					,		intermediate_size=self.intermediate_size					,		hidden_act=self.hidden_act					,		hidden_dropout_prob=self.hidden_dropout_prob					,		attention_probs_dropout_prob=self.attention_probs_dropout_prob					,		max_position_embeddings=self.max_position_embeddings					,		type_vocab_size=self.type_vocab_size					,		is_decoder=__snake_case					,		initializer_range=self.initializer_range					,		pad_token_id=self.pad_token_id					,		)






		def SCREAMING_SNAKE_CASE_      (    self   :  str   )					->       Tuple:
								'''simple docstring'''







								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase								=  self.prepare_config_and_inputs()

								UpperCAmelCase								=  True

								return config, input_ids, input_mask, token_labels






		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[int]					,		UpperCamelCase__   :  int					,		UpperCamelCase__   :  Tuple					,		UpperCamelCase__   :  Any   )					->       Any:
								'''simple docstring'''







								UpperCAmelCase								=  GPTNeoXModel(config=__snake_case   )
								model.to(__snake_case   )
								model.eval()
								UpperCAmelCase								=  model(__snake_case					,		attention_mask=__snake_case   )
								UpperCAmelCase								=  model(__snake_case   )
								self.parent.assertEqual(result.last_hidden_state.shape					,		(self.batch_size, self.seq_length, self.hidden_size)   )






		def SCREAMING_SNAKE_CASE_      (    self   :  str					,		UpperCamelCase__   :  Tuple					,		UpperCamelCase__   :  Optional[Any]					,		UpperCamelCase__   :  Any   )					->       str:
								'''simple docstring'''







								UpperCAmelCase								=  True
								UpperCAmelCase								=  GPTNeoXModel(__snake_case   )
								model.to(__snake_case   )
								model.eval()
								UpperCAmelCase								=  model(__snake_case					,		attention_mask=__snake_case   )
								self.parent.assertEqual(result.last_hidden_state.shape					,		(self.batch_size, self.seq_length, self.hidden_size)   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Union[str, Any]					,		UpperCamelCase__   :  List[str]					,		UpperCamelCase__   :  Tuple					,		UpperCamelCase__   :  Optional[Any]					,		UpperCamelCase__   :  Optional[Any]   )					->       Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase								=  GPTNeoXForCausalLM(config=__snake_case   )
								model.to(__snake_case   )
								model.eval()
								UpperCAmelCase								=  model(__snake_case					,		attention_mask=__snake_case					,		labels=__snake_case   )
								self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.seq_length, self.vocab_size)   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Any					,		UpperCamelCase__   :  List[Any]					,		UpperCamelCase__   :  Optional[Any]					,		UpperCamelCase__   :  Any					,		UpperCamelCase__   :  List[str]   )					->       Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase								=  self.num_labels
								UpperCAmelCase								=  GPTNeoXForQuestionAnswering(__snake_case   )
								model.to(__snake_case   )
								model.eval()
								UpperCAmelCase								=  model(__snake_case					,		attention_mask=__snake_case   )
								self.parent.assertEqual(result.start_logits.shape					,		(self.batch_size, self.seq_length)   )
								self.parent.assertEqual(result.end_logits.shape					,		(self.batch_size, self.seq_length)   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Union[str, Any]					,		UpperCamelCase__   :  Tuple					,		UpperCamelCase__   :  List[Any]					,		UpperCamelCase__   :  Union[str, Any]					,		UpperCamelCase__   :  Optional[int]   )					->       Any:
								'''simple docstring'''







								UpperCAmelCase								=  self.num_labels
								UpperCAmelCase								=  GPTNeoXForSequenceClassification(__snake_case   )
								model.to(__snake_case   )
								model.eval()
								UpperCAmelCase								=  ids_tensor([self.batch_size]					,		self.type_sequence_label_size   )
								UpperCAmelCase								=  model(__snake_case					,		attention_mask=__snake_case					,		labels=__snake_case   )
								self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.num_labels)   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Any					,		UpperCamelCase__   :  Optional[Any]					,		UpperCamelCase__   :  List[str]					,		UpperCamelCase__   :  Union[str, Any]					,		UpperCamelCase__   :  Dict   )					->       List[str]:
								'''simple docstring'''







								UpperCAmelCase								=  self.num_labels
								UpperCAmelCase								=  GPTNeoXForTokenClassification(__snake_case   )
								model.to(__snake_case   )
								model.eval()
								UpperCAmelCase								=  model(__snake_case					,		attention_mask=__snake_case					,		labels=__snake_case   )
								self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.seq_length, self.num_labels)   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Tuple					,		UpperCamelCase__   :  List[Any]					,		UpperCamelCase__   :  int					,		UpperCamelCase__   :  List[str]   )					->       Any:
								'''simple docstring'''







								UpperCAmelCase								=  True
								UpperCAmelCase								=  GPTNeoXForCausalLM(config=__snake_case   )
								model.to(__snake_case   )
								model.eval()

								# first forward pass
								UpperCAmelCase								=  model(__snake_case					,		attention_mask=__snake_case					,		use_cache=__snake_case   )
								UpperCAmelCase								=  outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								UpperCAmelCase								=  ids_tensor((self.batch_size, 3)					,		config.vocab_size   )
								UpperCAmelCase								=  ids_tensor((self.batch_size, 3)					,		vocab_size=2   )

								# append to next input_ids and
								UpperCAmelCase								=  torch.cat([input_ids, next_tokens]					,		dim=-1   )
								UpperCAmelCase								=  torch.cat([input_mask, next_mask]					,		dim=-1   )

								UpperCAmelCase								=  model(__snake_case					,		attention_mask=__snake_case					,		output_hidden_states=__snake_case   )
								UpperCAmelCase								=  output_from_no_past["hidden_states"][0]
								UpperCAmelCase								=  model(
								    __snake_case					,		attention_mask=__snake_case					,		past_key_values=__snake_case					,		output_hidden_states=__snake_case					,		)["hidden_states"][0]

								# select random slice
								UpperCAmelCase								=  ids_tensor((1,)					,		output_from_past.shape[-1]   ).item()
								UpperCAmelCase								=  output_from_no_past[:, -3:, random_slice_idx].detach()
								UpperCAmelCase								=  output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]   )

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(__snake_case					,		__snake_case					,		atol=1e-3   )   )







		def SCREAMING_SNAKE_CASE_      (    self   :  List[str]   )					->       Optional[int]:
								'''simple docstring'''







								UpperCAmelCase								=  self.prepare_config_and_inputs()
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase								=  config_and_inputs
								UpperCAmelCase								=  {"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict





@require_torch
class 			__magic_name__ (				snake_case__,	snake_case__,	snake_case__,	unittest.TestCase    ):
		lowercase   :							Optional[Any]        =(
		    (
		        GPTNeoXModel,
		        GPTNeoXForCausalLM,
		        GPTNeoXForQuestionAnswering,
		        GPTNeoXForSequenceClassification,
		        GPTNeoXForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		lowercase   :							List[str]        =(GPTNeoXForCausalLM,) if is_torch_available() else ()
		lowercase   :							str        =(
		    {
		        '''feature-extraction''': GPTNeoXModel,
		        '''question-answering''': GPTNeoXForQuestionAnswering,
		        '''text-classification''': GPTNeoXForSequenceClassification,
		        '''text-generation''': GPTNeoXForCausalLM,
		        '''token-classification''': GPTNeoXForTokenClassification,
		        '''zero-shot''': GPTNeoXForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		lowercase   :							Dict        =False
		lowercase   :							str        =False
		lowercase   :							Optional[Any]        =False
		lowercase   :							Optional[int]        =False






		def SCREAMING_SNAKE_CASE_      (    self   :  int   )					->       Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase								=  GPTNeoXModelTester(self   )
								UpperCAmelCase								=  ConfigTester(self					,		config_class=__snake_case					,		hidden_size=64					,		num_attention_heads=8   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Any   )					->       Tuple:
								'''simple docstring'''







								self.config_tester.run_common_tests()






		def SCREAMING_SNAKE_CASE_      (    self   :  Tuple   )					->       Tuple:
								'''simple docstring'''







								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase								=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(__snake_case					,		__snake_case					,		__snake_case   )






		def SCREAMING_SNAKE_CASE_      (    self   :  List[str]   )					->       Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase								=  self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(__snake_case					,		__snake_case					,		__snake_case   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Union[str, Any]   )					->       Optional[int]:
								'''simple docstring'''







								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase								=  self.model_tester.prepare_config_and_inputs_for_decoder()

								UpperCAmelCase								=  None

								self.model_tester.create_and_check_model_as_decoder(__snake_case					,		__snake_case					,		__snake_case   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[Any]   )					->       Any:
								'''simple docstring'''







								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase								=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(__snake_case					,		__snake_case					,		__snake_case   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Union[str, Any]   )					->       Dict:
								'''simple docstring'''







								UpperCAmelCase								=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*__snake_case   )






		def SCREAMING_SNAKE_CASE_      (    self   :  str   )					->       Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase								=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*__snake_case   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[Any]   )					->       Any:
								'''simple docstring'''







								UpperCAmelCase								=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*__snake_case   )






		def SCREAMING_SNAKE_CASE_      (    self   :  int   )					->       List[Any]:
								'''simple docstring'''







								UpperCAmelCase								=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*__snake_case   )






		@unittest.skip(reason="Feed forward chunking is not implemented"   )
		def SCREAMING_SNAKE_CASE_      (    self   :  str   )					->       List[str]:
								'''simple docstring'''







								pass







		@parameterized.expand([("linear",), ("dynamic",)]   )
		def SCREAMING_SNAKE_CASE_      (    self   :  List[Any]					,		UpperCamelCase__   :  Any   )					->       List[str]:
								'''simple docstring'''







								UpperCAmelCase							,		UpperCAmelCase								=  self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase								=  ids_tensor([1, 10]					,		config.vocab_size   )
								UpperCAmelCase								=  ids_tensor([1, int(config.max_position_embeddings * 1.5   )]					,		config.vocab_size   )

								set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase								=  GPTNeoXModel(__snake_case   )
								original_model.to(__snake_case   )
								original_model.eval()
								UpperCAmelCase								=  original_model(__snake_case   ).last_hidden_state
								UpperCAmelCase								=  original_model(__snake_case   ).last_hidden_state

								set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase								=  {"type": scaling_type, "factor": 10.0}
								UpperCAmelCase								=  GPTNeoXModel(__snake_case   )
								scaled_model.to(__snake_case   )
								scaled_model.eval()
								UpperCAmelCase								=  scaled_model(__snake_case   ).last_hidden_state
								UpperCAmelCase								=  scaled_model(__snake_case   ).last_hidden_state

								# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
								# maximum sequence length, so the outputs for the short input should match.
								if scaling_type == "dynamic":
														self.assertTrue(torch.allclose(__snake_case					,		__snake_case					,		atol=1e-5   )   )
								else:
														self.assertFalse(torch.allclose(__snake_case					,		__snake_case					,		atol=1e-5   )   )

								# The output should be different for long inputs
								self.assertFalse(torch.allclose(__snake_case					,		__snake_case					,		atol=1e-5   )   )





@require_torch
class 			__magic_name__ (				unittest.TestCase    ):






		@slow
		def SCREAMING_SNAKE_CASE_      (    self   :  Dict   )					->       Any:
								'''simple docstring'''







								UpperCAmelCase								=  AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped"   )
								for checkpointing in [True, False]:
														UpperCAmelCase								=  GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped"   )

														if checkpointing:
																				model.gradient_checkpointing_enable()
														else:
																				model.gradient_checkpointing_disable()
														model.to(__snake_case   )

														UpperCAmelCase								=  tokenizer("My favorite food is"					,		return_tensors="pt"   ).to(__snake_case   )
														# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
														# See: https://github.com/huggingface/transformers/pull/24193
														UpperCAmelCase								=  "My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure"

														UpperCAmelCase								=  model.generate(**__snake_case					,		do_sample=__snake_case					,		max_new_tokens=20   )
														UpperCAmelCase								=  tokenizer.batch_decode(__snake_case   )[0]

														self.assertEqual(__snake_case					,		__snake_case   )







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE     :					Optional[Any]      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					int      =       {
    "facebook/deit-base-distilled-patch16-224": (
        "https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						'deit'
    def __init__(			self     ,   __snake_case=7_6_8     ,   __snake_case=1_2     ,   __snake_case=1_2     ,   __snake_case=3_0_7_2     ,   __snake_case="gelu"     ,   __snake_case=0.0     ,   __snake_case=0.0     ,   __snake_case=0.02     ,   __snake_case=1E-12     ,   __snake_case=2_2_4     ,   __snake_case=1_6     ,   __snake_case=3     ,   __snake_case=True     ,   __snake_case=1_6     ,   **__snake_case     ,   ):
         super().__init__(**__snake_case       )

         snake_case											=							hidden_size
         snake_case											=							num_hidden_layers
         snake_case											=							num_attention_heads
         snake_case											=							intermediate_size
         snake_case											=							hidden_act
         snake_case											=							hidden_dropout_prob
         snake_case											=							attention_probs_dropout_prob
         snake_case											=							initializer_range
         snake_case											=							layer_norm_eps
         snake_case											=							image_size
         snake_case											=							patch_size
         snake_case											=							num_channels
         snake_case											=							qkv_bias
         snake_case											=							encoder_stride






class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						version.parse('1.11'      )
    @property
    def 						a_     (			self       ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]       )




    @property
    def 						a_     (			self       ):
         return 1E-4




